


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__			:      Dict								=get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						XLMRobertaTokenizer
							_UpperCAmelCase      =						XLMRobertaTokenizerFast
							_UpperCAmelCase      =						True
							_UpperCAmelCase      =						True






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().setUp()

										# We have a SentencePiece fixture for testing
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					XLMRobertaTokenizer(lowerCAmelCase__			,						keep_accents=lowerCAmelCase__       )
										tokenizer.save_pretrained(self.tmpdirname       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					'<pad>'
										SCREAMING_SNAKE_CASE_		:     List[str]					=					1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__       )			,						lowerCAmelCase__       )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__       )			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					list(self.get_tokenizer().get_vocab().keys()       )

										self.assertEqual(vocab_keys[0]			,						'<s>'       )
										self.assertEqual(vocab_keys[1]			,						'<pad>'       )
										self.assertEqual(vocab_keys[-1]			,						'<mask>'       )
										self.assertEqual(len(lowerCAmelCase__       )			,						1_0_0_2       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.assertEqual(self.get_tokenizer().vocab_size			,						1_0_0_2       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					XLMRobertaTokenizer(lowerCAmelCase__			,						keep_accents=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     int					=					tokenizer.tokenize('This is a test'       )
										self.assertListEqual(lowerCAmelCase__			,						['▁This', '▁is', '▁a', '▁t', 'est']       )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )			,						[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]			,						)

										SCREAMING_SNAKE_CASE_		:     str					=					tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
										self.assertListEqual(
										    lowerCAmelCase__			,						[
										        SPIECE_UNDERLINE + 'I',
										        SPIECE_UNDERLINE + 'was',
										        SPIECE_UNDERLINE + 'b',
										        'or',
										        'n',
										        SPIECE_UNDERLINE + 'in',
										        SPIECE_UNDERLINE + '',
										        '9',
										        '2',
										        '0',
										        '0',
										        '0',
										        ',',
										        SPIECE_UNDERLINE + 'and',
										        SPIECE_UNDERLINE + 'this',
										        SPIECE_UNDERLINE + 'is',
										        SPIECE_UNDERLINE + 'f',
										        'al',
										        's',
										        'é',
										        '.',
										    ]			,						)
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tokenizer.convert_tokens_to_ids(lowerCAmelCase__       )
										self.assertListEqual(
										    lowerCAmelCase__			,						[
										        value + tokenizer.fairseq_offset
										        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
										        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
										    ]			,						)

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.convert_ids_to_tokens(lowerCAmelCase__       )
										self.assertListEqual(
										    lowerCAmelCase__			,						[
										        SPIECE_UNDERLINE + 'I',
										        SPIECE_UNDERLINE + 'was',
										        SPIECE_UNDERLINE + 'b',
										        'or',
										        'n',
										        SPIECE_UNDERLINE + 'in',
										        SPIECE_UNDERLINE + '',
										        '<unk>',
										        '2',
										        '0',
										        '0',
										        '0',
										        ',',
										        SPIECE_UNDERLINE + 'and',
										        SPIECE_UNDERLINE + 'this',
										        SPIECE_UNDERLINE + 'is',
										        SPIECE_UNDERLINE + 'f',
										        'al',
										        's',
										        '<unk>',
										        '.',
										    ]			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										if not self.test_slow_tokenizer:
													# as we don't have a slow version, we can't compare the outputs between slow and fast versions
													return

										SCREAMING_SNAKE_CASE_		:     str					=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					self.tokenizer_class.from_pretrained(lowerCAmelCase__			,						**lowerCAmelCase__       )

																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE_		:     str					=					tokenizer_r.save_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_p.save_pretrained(lowerCAmelCase__       )

																# Checks it save with the same files + the tokenizer.json file for the fast one
																self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f       )
																self.assertSequenceEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer_r.from_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_p.from_pretrained(lowerCAmelCase__       )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )
																			# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																			# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																shutil.rmtree(lowerCAmelCase__       )

																# Save tokenizer rust, legacy_format=True
																SCREAMING_SNAKE_CASE_		:     int					=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r.save_pretrained(lowerCAmelCase__			,						legacy_format=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_p.save_pretrained(lowerCAmelCase__       )

																# Checks it save with the same files
																self.assertSequenceEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r.from_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     str					=					tokenizer_p.from_pretrained(lowerCAmelCase__       )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )

																shutil.rmtree(lowerCAmelCase__       )

																# Save tokenizer rust, legacy_format=False
																SCREAMING_SNAKE_CASE_		:     Any					=					tempfile.mkdtemp()

																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_r.save_pretrained(lowerCAmelCase__			,						legacy_format=lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     int					=					tokenizer_p.save_pretrained(lowerCAmelCase__       )

																# Checks it saved the tokenizer.json file
																self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files       )       )

																# Checks everything loads correctly in the same way
																SCREAMING_SNAKE_CASE_		:     List[Any]					=					tokenizer_r.from_pretrained(lowerCAmelCase__       )
																SCREAMING_SNAKE_CASE_		:     Tuple					=					tokenizer_p.from_pretrained(lowerCAmelCase__       )

																# Check special tokens are set accordingly on Rust and Python
																for key in tokenizer_pp.special_tokens_map:
																			self.assertTrue(hasattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )

																shutil.rmtree(lowerCAmelCase__       )






							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(lowerCAmelCase__			,						f.name       )
													SCREAMING_SNAKE_CASE_		:     List[str]					=					XLMRobertaTokenizer(f.name			,						keep_accents=lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					pickle.dumps(lowerCAmelCase__       )
										pickle.loads(lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										if not self.test_rust_tokenizer:
													return

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_tokenizer()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_rust_tokenizer()

										SCREAMING_SNAKE_CASE_		:     Any					=					'I was born in 92000, and this is falsé.'

										SCREAMING_SNAKE_CASE_		:     List[str]					=					tokenizer.tokenize(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					rust_tokenizer.tokenize(lowerCAmelCase__       )
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					rust_tokenizer.encode(lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__       )
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.get_rust_tokenizer()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer.encode(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					rust_tokenizer.encode(lowerCAmelCase__       )
										self.assertListEqual(lowerCAmelCase__			,						lowerCAmelCase__       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'Hello World!'
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[0, 3_5_3_7_8, 6_6_6_1, 3_8, 2]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(lowerCAmelCase__			,						self.big_tokenizer.encode(lowerCAmelCase__       )       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
										    'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
										    ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
										)
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					[
										    0,
										    3_2_9_3,
										    8_3,
										    1_0,
										    4_5_5_2,
										    4_9_8_9,
										    7_9_8_6,
										    6_7_8,
										    1_0,
										    5_9_1_5,
										    1_1_1,
										    1_7_9_4_5_9,
										    1_2_4_8_5_0,
										    4,
										    6_0_4_4,
										    2_3_7,
										    1_2,
										    6,
										    5,
										    6,
										    4,
										    6_7_8_0,
										    7_0_5,
										    1_5,
										    1_3_8_8,
										    4_4,
										    3_7_8,
										    1_0_1_1_4,
										    7_1_1,
										    1_5_2,
										    2_0,
										    6,
										    5,
										    2_2_3_7_6,
										    6_4_2,
										    1_2_2_1,
										    1_5_1_9_0,
										    3_4_1_5_3,
										    4_5_0,
										    5_6_0_8,
										    9_5_9,
										    1_1_1_9,
										    5_7_7_0_2,
										    1_3_6,
										    1_8_6,
										    4_7,
										    1_0_9_8,
										    2_9_3_6_7,
										    4_7,
										    # 4426, # What fairseq tokenizes from "<unk>": "_<"
										    # 3678, # What fairseq tokenizes from "<unk>": "unk"
										    # 2740, # What fairseq tokenizes from "<unk>": ">"
										    3,  # What we tokenize from "<unk>": "<unk>"
										    6,  # Residue from the tokenization: an extra sentencepiece underline
										    4,
										    6_0_4_4,
										    2_3_7,
										    6_2_8_4,
										    5_0_9_0_1,
										    5_2_8,
										    3_1,
										    9_0,
										    3_4,
										    9_2_7,
										    2,
										]
										# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
										# xlmr.eval()
										# xlmr.encode(symbols)

										self.assertListEqual(lowerCAmelCase__			,						self.big_tokenizer.encode(lowerCAmelCase__       )       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					{'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=lowerCAmelCase__			,						model_name='xlm-roberta-base'			,						revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'			,						)







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


__lowerCAmelCase     :	Dict       			=  logging.getLogger(__name__)




@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42







@dataclass
class 			UpperCAmelCase_     :


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						None
  a__       			=						None







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""train"""
  a__       			=						"""dev"""
  a__       			=						"""test"""







class 			UpperCAmelCase_     :


  '''simple docstring'''







  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					Union[Split, str]       )     ->      List[InputExample]:







    """simple docstring"""






    raise NotImplementedError
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					str       )     ->      List[str]:







    """simple docstring"""






    raise NotImplementedError





  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					List[InputExample]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Tuple="[CLS]"     ,				UpperCamelCase__     :					List[str]=1     ,				UpperCamelCase__     :					str="[SEP]"     ,				UpperCamelCase__     :					int=False     ,				UpperCamelCase__     :					Dict=False     ,				UpperCamelCase__     :					List[str]=0     ,				UpperCamelCase__     :					str=0     ,				UpperCamelCase__     :					Optional[Any]=-100     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					Optional[int]=True     ,				)     ->      List[InputFeatures]:







    """simple docstring"""






    __magic_name__								=			{label: i for i, label in enumerate(UpperCamelCase__       )}

    __magic_name__								=			[]
    for ex_index, example in enumerate(UpperCamelCase__       ):
      if ex_index % 1_0000 == 0:
        logger.info("""Writing example %d of %d"""     ,				UpperCamelCase__     ,				len(UpperCamelCase__       )       )

      __magic_name__								=			[]
      __magic_name__								=			[]
      for word, label in zip(example.words     ,				example.labels       ):
        __magic_name__								=			tokenizer.tokenize(UpperCamelCase__       )

        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
        if len(UpperCamelCase__       ) > 0:
          tokens.extend(UpperCamelCase__       )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase__       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
      __magic_name__								=			tokenizer.num_special_tokens_to_add()
      if len(UpperCamelCase__       ) > max_seq_length - special_tokens_count:
        __magic_name__								=			tokens[: (max_seq_length - special_tokens_count)]
        __magic_name__								=			label_ids[: (max_seq_length - special_tokens_count)]

      # The convention in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
      # (b) For single sequences:
      #  tokens:   [CLS] the dog is hairy . [SEP]
      #  type_ids:   0   0   0   0  0     0   0
      #
      # Where "type_ids" are used to indicate whether this is the first
      # sequence or the second sequence. The embedding vectors for `type=0` and
      # `type=1` were learned during pre-training and are added to the wordpiece
      # embedding vector (and position vector). This is not *strictly* necessary
      # since the [SEP] token unambiguously separates the sequences, but it makes
      # it easier for the model to learn the concept of sequences.
      #
      # For classification tasks, the first vector (corresponding to [CLS]) is
      # used as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens += [sep_token]
      label_ids += [pad_token_label_id]
      if sep_token_extra:
        # roberta uses an extra separator b/w pairs of sentences
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
      __magic_name__								=			[sequence_a_segment_id] * len(UpperCamelCase__       )

      if cls_token_at_end:
        tokens += [cls_token]
        label_ids += [pad_token_label_id]
        segment_ids += [cls_token_segment_id]
      else:
        __magic_name__								=			[cls_token] + tokens
        __magic_name__								=			[pad_token_label_id] + label_ids
        __magic_name__								=			[cls_token_segment_id] + segment_ids

      __magic_name__								=			tokenizer.convert_tokens_to_ids(UpperCamelCase__       )

      # The mask has 1 for real tokens and 0 for padding tokens. Only real
      # tokens are attended to.
      __magic_name__								=			[1 if mask_padding_with_zero else 0] * len(UpperCamelCase__       )

      # Zero-pad up to the sequence length.
      __magic_name__								=			max_seq_length - len(UpperCamelCase__       )
      if pad_on_left:
        __magic_name__								=			([pad_token] * padding_length) + input_ids
        __magic_name__								=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
        __magic_name__								=			([pad_token_segment_id] * padding_length) + segment_ids
        __magic_name__								=			([pad_token_label_id] * padding_length) + label_ids
      else:
        input_ids += [pad_token] * padding_length
        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
        segment_ids += [pad_token_segment_id] * padding_length
        label_ids += [pad_token_label_id] * padding_length

      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length
      assert len(UpperCamelCase__       ) == max_seq_length

      if ex_index < 5:
        logger.info("""*** Example ***"""       )
        logger.info("""guid: %s"""     ,				example.guid       )
        logger.info("""tokens: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in tokens]       )       )
        logger.info("""input_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_ids]       )       )
        logger.info("""input_mask: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in input_mask]       )       )
        logger.info("""segment_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in segment_ids]       )       )
        logger.info("""label_ids: %s"""     ,				""" """.join([str(UpperCamelCase__       ) for x in label_ids]       )       )

      if "token_type_ids" not in tokenizer.model_input_names:
        __magic_name__								=			None

      features.append(
          InputFeatures(
              input_ids=UpperCamelCase__     ,				attention_mask=UpperCamelCase__     ,				token_type_ids=UpperCamelCase__     ,				label_ids=UpperCamelCase__       )       )
    return features


if is_torch_available():
    import torch
    from torch import nn
    from torch.utils.data import Dataset




    class 			UpperCAmelCase_     (					_A	):


      '''simple docstring'''







      a__       			=						42
      a__       			=						nn.CrossEntropyLoss().ignore_index
      def __init__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      str:







        """simple docstring"""






        __magic_name__								=			os.path.join(
            UpperCamelCase__     ,				"""cached_{}_{}_{}""".format(mode.value     ,				tokenizer.__class__.__name__     ,				str(UpperCamelCase__       )       )     ,				)

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        __magic_name__								=			cached_features_file + """.lock"""
        with FileLock(UpperCamelCase__       ):
          if os.path.exists(UpperCamelCase__       ) and not overwrite_cache:
            logger.info(F'''Loading features from cached file {cached_features_file}'''       )
            __magic_name__								=			torch.load(UpperCamelCase__       )
          else:
            logger.info(F'''Creating features from dataset file at {data_dir}'''       )
            __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
            # TODO clean up all this to leverage built-in features of tokenizers
            __magic_name__								=			token_classification_task.convert_examples_to_features(
                UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)
            logger.info(F'''Saving features into cached file {cached_features_file}'''       )
            torch.save(self.features     ,				UpperCamelCase__       )
      def __len__(					self     :					List[str]       )     ->      Any:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]


if is_tf_available():
    import tensorflow as tf




    class 			UpperCAmelCase_     :


      '''simple docstring'''







      a__       			=						42
      a__       			=						-1_00
      def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					TokenClassificationTask     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					PreTrainedTokenizer     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					List[Any]=False     ,				UpperCamelCase__     :					Split = Split.train     ,				)     ->      List[Any]:







        """simple docstring"""






        __magic_name__								=			token_classification_task.read_examples_from_file(UpperCamelCase__     ,				UpperCamelCase__       )
        # TODO clean up all this to leverage built-in features of tokenizers
        __magic_name__								=			token_classification_task.convert_examples_to_features(
            UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				cls_token_at_end=bool(model_type in ["""xlnet"""]       )     ,				cls_token=tokenizer.cls_token     ,				cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,				sep_token=tokenizer.sep_token     ,				sep_token_extra=UpperCamelCase__     ,				pad_on_left=bool(tokenizer.padding_side == """left"""       )     ,				pad_token=tokenizer.pad_token_id     ,				pad_token_segment_id=tokenizer.pad_token_type_id     ,				pad_token_label_id=self.pad_token_label_id     ,				)

        def gen():
          for ex in self.features:
            if ex.token_type_ids is None:
              yield (
                  {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                  ex.label_ids,
              )
            else:
              yield (
                  {
                      "input_ids": ex.input_ids,
                      "attention_mask": ex.attention_mask,
                      "token_type_ids": ex.token_type_ids,
                  },
                  ex.label_ids,
              )

        if "token_type_ids" not in tokenizer.model_input_names:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,				(
                  {"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )},
                  tf.TensorShape([None]       ),
              )     ,				)
        else:
          __magic_name__								=			tf.data.Dataset.from_generator(
              UpperCamelCase__     ,				({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,				(
                  {
                      """input_ids""": tf.TensorShape([None]       ),
                      """attention_mask""": tf.TensorShape([None]       ),
                      """token_type_ids""": tf.TensorShape([None]       ),
                  },
                  tf.TensorShape([None]       ),
              )     ,				)
      def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







        """simple docstring"""






        __magic_name__								=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

        return self.dataset
      def __len__(					self     :					Tuple       )     ->      Tuple:







        """simple docstring"""






        return len(self.features       )





      def __getitem__(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      InputFeatures:







        """simple docstring"""






        return self.features[i]

